Kiran Pachlasiya
@pimrbhopal.ac.in
Assistant Professor CSE
Kiran Pachlasiya having 10+ year of experience in academics also a researcher
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Computer Science, Computer Engineering, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sarthak Nahar, Divyam Pithawa, Vivek Bhardwaj, Romil Rawat, Anjali Rawat, and Kiran Pachlasiya
Wiley
Romil Rawat, Bhagwati Garg, Vinod Mahor, Shrikant Telang, Kiran Pachlasiya, and Mukesh Chouhan
Wiley
Romil Rawat, Vinod Mahor, Bhagwati Garg, Mukesh Chouhan, Kiran Pachlasiya, and Shrikant Telang
Elsevier
Romil Rawat, Vinod Mahor, Mukesh Chouhan, Kiran Pachlasiya, Shrikant Telang, and Bhagwati Garg
Springer Nature Singapore
Vinod Mahor, Kiran Pachlasiya, Bhagwati Garg, Mukesh Chouhan, Shrikant Telang, and Romil Rawat
Springer Nature Singapore
Vinod Mahor, Bhagwati Garg, Shrikant Telang, Kiran Pachlasiya, Mukesh Chouhan, and Romil Rawat
Springer Nature Singapore
Romil Rawat, Bhagwati Garg, Kiran Pachlasiya, Vinod Mahor, Shrikant Telang, Mukesh Chouhan, Surendra Kumar Shukla, and Rina Mishra
IGI Global
Real-time network inspection applications face a threat of vulnerability as high-speed networks continue to expand. For companies and ISPs, real-time traffic classification is an issue. The classifier monitor is made up of three modules: Capturing_of_Packets (CoP) and pre-processing, Reconciliation_of_Flow (RoF), and categorization of Machine Learning (ML). Based on parallel processing along with well-defined interfacing of data, the modules are framed, allowing each module to be modified and upgraded separately. The Reconciliation_of_Flow (RoF) mechanism becomes the output bottleneck in this pipeline. In this implementation, an optimal reconciliation process was used, resulting in an average delivery time of 0.62 seconds. In order to verify our method, we equated the results of the AdaBoost Ensemble Learning Algorithm (ABELA), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), and Flexible Naive Bayes (FNB) in the classification module. The architectural design of the run time CSNTA categorization (flow-based) scheme is presented in this paper.
Romil Rawat, Vinod Mahor, Bhagwati Garg, Shrikant Telang, Kiran Pachlasiya, Anil Kumar, Surendra Kumar Shukla, and Megha Kuliha
IGI Global
One of the most critical activities of revealing terrorism-related information is classifying online documents.The internet provides consumers with a variety of useful knowledge, and the volume of web material is increasingly growing. This makes finding potentially hazardous records incredibly difficult. To define the contents, merely extracting keywords from records is inadequate. Many methods have been studied so far to develop automatic document classification systems, they are mainly computational and knowledge-based approaches. due to the complexities of natural languages, these approaches do not provide sufficient results. To fix this shortcoming, we given approach of structure dependent on the WordNet hierarchy and the frequency of n-gram data that employs word similarity. Using four different queries terms from four different regions, this approach was checked for the NY Times articles that were sampled. Our suggested approach successfully removes background words and phrases from the document recognizes connected to terrorism texts, according to experimental findings.